Method of adaptive estimation of adhesion coefficient of vehicle road surface considering complex excitation conditions

ABSTRACT

A method for adaptive estimation of a road surface adhesion coefficient for a vehicle with complex excitation conditions taken into consideration comprises the following steps: 1) designing an estimator according to a single-wheel dynamics model of a vehicle, and estimating a longitudinal tire force and a road surface peak adhesion coefficient under longitudinal excitation; 2) designing an estimator according to a two-degree-of-freedom kinematic model of the vehicle, and estimating a tire aligning moment and a road surface peak adhesion coefficient under excitation of a lateral force; and 3) determining an excitation condition met by the vehicle according to a vehicle state parameter, performing fuzzy inference to obtain limits achievable by current longitudinal and lateral tire forces, and designing a fusion observer to fuse estimation results. The method achieves favorable robustness, improves real-time capability, and can be performed quickly and accurately.

FIELD OF TECHNOLOGY

The invention relates to a field of automobile control, in particular toa method of adaptive estimation of an adhesion coefficient of a vehicleroad surface considering complex excitation conditions.

BACKGROUND

A peak adhesion coefficient of a vehicle road surface is a key parameterto implement precise and high-quality motion control of an automobile.The current method is to construct a state observer under a condition oftire force excitation in a single direction. Such a method is unable toperform accurate estimation when the excitation is unmet. And also, whenlongitudinal-lateral coupling occurs in tire forces, a tire model isdistorted. In addition, an estimator has slow estimation convergence andlow robustness. Therefore, how to comprehensively utilize a road surfaceidentification method under longitudinal and lateral tire excitationforces will be a difficulty and focus of future research.

SUMMARY

The purpose of the present invention is to provide a method of adaptiveestimation of an adhesion coefficient of a vehicle road surfaceconsidering complex excitation conditions in order to overcome theabove-mentioned defects of the prior art.

The object of the present invention can be achieved through thefollowing technical solutions:

a method of adaptive estimation of an adhesion coefficient of a vehicleroad surface considering complex excitation conditions, the methodincluding the following steps:

1) designing an estimator based on a single-wheel dynamical model of awhole vehicle, and estimating a peak adhesion coefficient of the roadsurface under a longitudinal tire force and longitudinal excitation;

2) designing an estimator based on a two-degree-of-freedom kinematicmodel of the whole vehicle, and estimating the peak adhesion coefficientof the road surface under a tire aligning torque and lateral forceexcitation;

3) determining the excitation conditions met by the vehicle from vehiclestate parameters, obtaining limits that the current longitudinal andlateral tire forces can reach by fuzzy inference, and thereby designinga fusion observer to fuse estimation results.

In step 1), the single-wheel dynamical model of the whole vehicle is asfollows:

${\overset{˙}{\omega} = {\frac{1}{I_{w}}\left\lbrack {T_{m} - {{\mu_{x}\left( {\theta_{x},\lambda} \right)} \cdot F_{z} \cdot R}} \right\rbrack}}{\lambda = \left\{ \begin{matrix}{\frac{{\omega \cdot R} - v_{x}}{\omega \cdot R};{v_{x} < {\omega \cdot R}}} \\{\frac{v_{x} - {\omega \cdot R}}{v_{x}};{v_{x} \geq {\omega \cdot R}}}\end{matrix} \right.}$

wherein ω is an angular velocity of the wheel, {dot over (ω)} is anangular acceleration of the wheel, R is a radius of the wheel, T_(m) isa driving/braking torque acting on the wheel, F_(z) is a vertical loadacting on the wheel, I_(w) is a rotational inertia of the wheel, λ is aslip rate of the wheel, v_(x) is a longitudinal speed at a center of thewheel, and μ_(x)(θ_(x),λ) is a current utilization adhesion coefficientof the tire to the road surface obtained based on a tire model.

An expression of the tire model is as follows:

${\mu\left( {\theta,\lambda} \right)} = {\theta - {\theta e}^{{- \frac{c_{1}}{\theta}}{({\lambda + {c_{2}\lambda^{2}}})}} - {c_{3}{{\lambda sgn}(\lambda)}} + {c_{4}\lambda^{2}}}$

wherein θ is the peak adhesion coefficient of the road surface, i.e.,the peak adhesion coefficient of the road surface corresponding to ahighest point of a μ−λ curve, is a longitudinal slip stiffness of thetire, i.e., a slope of the μ−λ curve at an origin, and c₂, C₃ and c₄ arerespectively control parameters for a descending section of the curve ofthe peak adhesion coefficient of the road surface versus the slip rate.

In step 1), an expression for estimating the peak adhesion coefficientof the road surface under the longitudinal tire force and longitudinalexcitation is as follows:

${{\overset{\hat{}}{F}}_{x} = {{\frac{I_{w}}{R}\left( {y + {K\omega}} \right)} - {F_{z} \cdot {\mu_{x}\left( {{\overset{\hat{}}{\theta}}_{x},\lambda} \right)}}}}{\overset{.}{y} = {{{- \frac{K}{I_{w}}}\left( {T_{m} + {R\overset{\hat{}}{F_{x}}}} \right)} + {\frac{R}{I_{w}} \cdot \frac{\partial{\mu_{x}\left( {{\overset{\hat{}}{\theta}}_{x},\lambda} \right)}}{\partial\theta_{x}} \cdot {\overset{\overset{.}{\hat{}}}{\theta}}_{x}}}}{{\overset{\overset{.}{\hat{}}}{\theta}}_{x} = {\gamma\left\lbrack {{\theta_{x}\left( {\lambda,\hat{F_{x}}} \right)} - {\overset{\hat{}}{\theta}}_{x}} \right\rbrack}}$

wherein {circumflex over (F)}_(x) is an estimated value of the tirelongitudinal force, μ_(x)({circumflex over (θ)}_(x),λ) is theutilization adhesion coefficient calculated based on an estimated valueof the adhesion coefficient of the road surface and the slip rate, K isa gain of a longitudinal force estimator, θ_(x)(λ,{circumflex over(F)}_(x)) is the peak adhesion coefficient of the road surfacecalculated from the curve described by the tire model based on a currentlongitudinal force and slip rate, {circumflex over (θ)}_(x) is anestimated value of the peak adhesion coefficient of the road surfaceunder longitudinal excitation, γ is a gain of an adhesion coefficientestimator of the road surface, y is an intermediate variable, {dot over(y)} is a derivative of y with respect to time, and {circumflex over({dot over (θ)})}_(x) is a derivative of {circumflex over (θ)}_(x) withrespect to time.

In step 2), the two-degree-of-freedom kinematic model of the wholevehicle is as follows:

${\alpha_{f} = {\beta + \frac{l_{f}R}{v_{0}} - \delta}}{\alpha_{r} = {\beta - \frac{l_{r}R}{v_{0}}}}$

wherein δ is a rotation angle of a front wheel, lf and lr arerespectively a distance from a center of the front wheel and of a rearwheel to a center of mass, v₀ is a longitudinal speed of the vehicle, βis a side slip angle of the vehicle, αf and αr are respectively a slipangle of the front wheel and of the rear wheel, and R is the radius ofthe wheel.

In step 2), an expression for estimating the peak adhesion coefficientof the road surface under the tire aligning torque and lateral forceexcitation is as follows:

{circumflex over (M)} _(k) =A{dot over (δ)} _(w) +B{umlaut over (δ)}_(w) +i _(s)(δ_(w))M _(s) +i _(m)(δ_(w))M _(m)

{circumflex over (M)} _(k) =f(α,F _(z))

{circumflex over ({dot over (θ)})}_(y) =k ₁ sgn({circumflex over (M)}_(k))·(M _(k) −{circumflex over (M)} _(k))+k ₂ sgn(â _(y))·(a _(y) −â_(y))

wherein α is a slip angle of the wheel, δ_(w) is a rotation angle of asteering wheel, i_(s)(δ_(w)) is a torque-to-rotation ratio of a boostermotor to a master pin, i_(m)(δ_(w)) is a torque-to-rotation ratio of thesteering wheel to the master pin, M_(m) is a torque applied to thesteering wheel, M_(s) is a torque of the booster motor, A and B arefitting parameters, M_(k) is a fitting total aligning torque,{circumflex over (M)}_(k) is an estimated value of the aligning torquecalculated based on the vertical load of the wheel and the slip angle,F_(z) is the vertical load applied on the wheel, â_(y) is an estimatedvalue of a lateral acceleration of the vehicle, a_(y) is an actual valueof the lateral acceleration of the vehicle, k₁ and k₂ are gains of theestimators, {circumflex over (θ)}₃, is an estimated value of the peakadhesion coefficient of the road surface under lateral force excitation,and {circumflex over ({dot over (θ)})}_(y) is a derivative of{circumflex over (θ)}_(y) with respect to time.

Step 3) includes:

31) obtaining a vehicle excitation state by fuzzy inference;

32) performing adaptive estimation of the peak adhesion coefficient ofthe road surface under complex excitation.

Step 31) is as follows:

inputting a membership function, taking a slip rate reference λ/C_(λ)and a slip angle reference α/C_(α) as input quantities, wherein C_(λ)and C_(α) are catastrophe points at which tire characteristics enter anonlinear zone and are respectively taken as the corresponding slip rateand slip angle at which the peak adhesion coefficient is reached, andtaking Ĉ₁, Ĉ₂ of different estimators as output quantities; setting[0,1] as a domain of both the input quantities and the outputquantities; and dividing the domain into corresponding intervalsrespectively having small, medium and large fuzzy membership degrees.

In step 32), an expression for performing adaptive estimation of thepeak adhesion coefficient of the road surface under complex excitationis as follows:

{circumflex over ({dot over (θ)})}={circumflex over ({dot over(θ)})}_(x)+{circumflex over ({dot over (θ)})}_(y)

{circumflex over ({dot over (θ)})}_(x)=γ[θ_(x)(λ,{circumflex over (F)}_(x))−C ₁·{circumflex over (θ)}]

{circumflex over ({dot over (θ)})}_(y) =k ₁ sgn({circumflex over (M)}_(k))·(M _(k) −Ĉ ₂ {circumflex over (M)} _(k))+k ₂ sgn(â _(y))·(a _(y)−Ĉ ₂ â _(y))

wherein Ĉ₁ a representative value of longitudinal sliding degree of thewheel, Ĉ₂ is a representative value of side slip degree of the wheel,and {circumflex over (θ)} is an estimated value of the peak adhesioncoefficient of the road surface.

Compared with the prior art, the present invention has the followingadvantages:

1. the estimation algorithm of an adhesion coefficient of a vehicle roadsurface designed by the present invention, under complex excitationforces, can determine longitudinal sliding and side slipping states of atire in real time, so as to make adaptive adjustments to a tire model,thereby ensuring that the estimation stably converges withoutdivergence;

2. the estimation algorithm of an adhesion coefficient of a vehicle roadsurface designed by the present invention, based on concurrentobservation of longitudinal sliding and side slipping states of thetire, can make confidence determination and fuse estimation results, andthus has superior real-time performance over currently existingestimation algorithms that can only use one of the excitation forces;and

3. the estimation algorithm of an adhesion coefficient of a vehicle roadsurface designed by the present invention, as early as in an initialstage of steering, can make fast and accurate estimation of the roadsurface according to an aligning torque.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart of a method according to the present invention;

FIG. 2 is a schematic diagram of a single wheel dynamical modelaccording to an embodiment;

FIG. 3 is a schematic diagram of a two-degree-of-freedom kinematic modelof a whole vehicle according to an embodiment; and

FIG. 4 is a schematic diagram of estimation of an aligning torqueaccording to an embodiment.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The present invention is described in detail below with reference to theaccompanying drawings and specific embodiments.

EMBODIMENTS

The present invention is described in detail below with reference to theaccompanying drawings and specific embodiments. Apparently, thedescribed embodiments are some, but not all, embodiments of the presentinvention. Based on the embodiments of the present invention, all ofother embodiments obtained by a person of ordinary skill in the artwithout any creative effort shall belong to the protection scope of thepresent invention.

EMBODIMENTS

As shown in FIG. 1, the present invention provides a method of adaptiveestimation of an adhesion coefficient of a vehicle road surfaceconsidering complex excitation conditions, the method including thefollowing steps:

Step 1, designing an estimator based on a single-wheel dynamical model,and estimating a peak adhesion coefficient of the road surface under alongitudinal tire force and longitudinal excitation. The processincludes:

1.1 establishing a single-wheel dynamical model of a whole vehicle.

First, obtaining a wheel angular velocity and a wheel slip rate:

${\overset{˙}{\omega} = {\frac{1}{I_{\omega}}\left\lbrack {T_{m} - {{\mu_{x}\left( {\theta_{x},\lambda} \right)} \cdot F_{z} \cdot R}} \right\rbrack}}{\lambda = \left\{ \begin{matrix}{\frac{{\omega \cdot R} - v_{x}}{\omega \cdot R};{v_{x} < {\omega \cdot R}}} \\{\frac{v_{x} - {\omega \cdot R}}{v_{x}};{v_{x} \geq {\omega \cdot R}}}\end{matrix} \right.}$

wherein ω is the angular velocity of the wheel, R is a radius of thewheel, T_(m) is a driving/braking torque acting on the wheel, F_(z) is avertical load acting on the wheel, I_(ω) is a rotational inertia of thewheel, λ is a slip rate of the wheel, v_(x) is a longitudinal speed at acenter of the wheel, and μ_(x) (θ_(x),λ) is a current utilizationadhesion coefficient of the tire to the road surface obtained based on atire model;

Then, expressing the tire model as:

${\mu\left( {\theta,\ \lambda} \right)} = {\theta - {\theta e^{{- \frac{c_{1}}{\theta}}{({\lambda + {c_{2}\lambda^{2}}})}}} - {c_{3}{{\lambda{sgn}}(\lambda)}} + {c_{4}\lambda^{2}}}$

wherein θ is the peak adhesion coefficient of the road surface, i.e.,the peak adhesion coefficient of the road surface corresponding to ahighest point of a μ−λ curve, λ is the slip rate of the wheel, c₁ is alongitudinal slip stiffness of the tire, i.e., a slope of the μ−λ curveat an origin, and c₂, c₃, and c₄ are respectively control parameters fora descending section of the curve of the peak adhesion coefficient ofthe road surface versus the slip rate.

1.2 An expression of an estimation algorithm of the peak adhesioncoefficient of the road surface under the longitudinal tire force andlongitudinal excitation is as follows:

${{\overset{\hat{}}{F}}_{x} = {{\frac{I_{w}}{r}\left( {y + {K\omega}} \right)} - {F_{z} \cdot {\mu_{x}\left( {{\overset{\hat{}}{\theta}}_{x},\lambda} \right)}}}}{\overset{.}{y} = {{{- \frac{K}{I_{w}}}\left( {T_{m} + {R{\overset{\hat{}}{F}}_{x}}} \right)} + {\frac{R}{I_{w}} \cdot \frac{\partial{\mu_{x}\left( {{\overset{\hat{}}{\theta}}_{x},\lambda} \right)}}{\partial\theta_{x}} \cdot {\overset{\overset{.}{\hat{}}}{\theta}}_{x}}}}{{\overset{\overset{.}{\hat{}}}{\theta}}_{x} = {\gamma\left\lbrack {{\theta_{x}\left( {\lambda,{\overset{\hat{}}{F}}_{x}} \right)} - {\overset{\hat{}}{\theta}}_{x}} \right\rbrack}}$

wherein {circumflex over (F)}_(x) is an estimated value of the tirelongitudinal force, μ_(x)({circumflex over (θ)}_(x),λ) is theutilization adhesion coefficient calculated based on an estimated valueof the adhesion coefficient of the road surface and the slip rate, K isa gain of a longitudinal force estimator, θ_(x)(λ,{circumflex over(F)}_(x)) is the peak adhesion coefficient of the road surfacecalculated by a numerical calculation method from the curve described bythe tire model based on a current longitudinal force and slip rate,{circumflex over (θ)}_(x) is an estimated value of the peak adhesioncoefficient of the road surface under longitudinal excitation, and γ isa gain of an adhesion coefficient estimator of the road surface.

Step 2, designing an estimator based on a two-degree-of-freedomkinematic model of the whole vehicle, and estimating the peak adhesioncoefficient of the road surface under a tire aligning torque and lateralforce excitation. The process includes:

2.1 Establishing the two-degree-of-freedom kinematic model of the wholevehicle.

Obtaining the slip angle of the wheel:

${\alpha_{f} = {\beta + \frac{l_{f}R}{v_{0}} - \delta}}{\alpha_{r} = {\beta - \frac{l_{r}R}{v_{0}}}}$

wherein δ is a rotation angle of a front wheel, l_(f) and l_(r) arerespectively a distance from a center of the front wheel and of a rearwheel to a center of mass, v₀ is a longitudinal speed of the vehicle, βis a side slip angle of the vehicle, and α_(f) and α_(r) arerespectively a slip angle of the front wheel and of the rear wheel.

2.2 The estimation algorithm of the adhesion coefficient of the roadsurface under longitudinal tire force and longitudinal excitation.

An expression is as follows:

${{\overset{\hat{}}{F}}_{x} = {{\frac{I_{w}}{r}\left( {y + {K\omega}} \right)} - {F_{z} \cdot {\mu_{x}\left( {{\overset{\hat{}}{\theta}}_{x},\lambda} \right)}}}}{\overset{.}{y} = {{{- \frac{K}{I_{w}}}\left( {T_{m} + {R{\overset{\hat{}}{F}}_{x}}} \right)} + {\frac{R}{I_{w}} \cdot \frac{\partial{\mu_{x}\left( {{\overset{\hat{}}{\theta}}_{x},\lambda} \right)}}{\partial\theta_{x}} \cdot {\overset{.}{\overset{\hat{}}{\theta}}}_{x}}}}{{\overset{\overset{.}{\hat{}}}{\theta}}_{x} = {\gamma\left\lbrack {{\theta_{x}\left( {\lambda,{\overset{\hat{}}{F}}_{x}} \right)} - {\overset{\hat{}}{\theta}}_{x}} \right\rbrack}}$

wherein {circumflex over (F)}_(x) is an estimated value of the tirelongitudinal force, μ_(x)({circumflex over (θ)}_(x),λ) is theutilization adhesion coefficient calculated based on an estimated valueof the adhesion coefficient of the road surface and the slip rate, K isa gain of a longitudinal force estimator, θ_(x)(λ,{circumflex over(F)}_(x)) is the peak adhesion coefficient of the road surfacecalculated by a numerical calculation method from the curve described bythe tire model based on a current longitudinal force and slip rate,{circumflex over (θ)}_(x) is an estimated value of the peak adhesioncoefficient of the road surface under longitudinal excitation, and γ isa gain of an adhesion coefficient estimator of the road surface.

Step 3, determining the excitation conditions met by the vehicle fromvehicle state parameters, obtaining limits that the current longitudinaland lateral tire forces can reach by fuzzy inference, and therebydesigning a fusion observer to fuse estimation results. The processincludes:

3.1 Fuzzy inference of vehicle excitation states.

Inputting a membership function, taking a slip rate reference λ/C_(λ)and a slip angle reference α/C_(α) as input quantities, wherein C_(λ)and C_(α) are catastrophe points at which tire characteristics enter anonlinear zone and are respectively taken as the corresponding slip rateand slip angle at which the peak adhesion coefficient is reached, andboth of the two items are obtained in real time through numericalcalculation based on {circumflex over (θ)}; and taking Ĉ₁, Ĉ₂ ofdifferent estimators as output quantities. Setting [0,1] as a domain ofboth the input quantities and the output quantities; and dividing thedomain into corresponding intervals respectively having S, M and B(respectively corresponding to small, medium and large) fuzzy membershipdegrees.

3.2 An adaptive estimation algorithm of the peak adhesion coefficient ofthe road surface under complex excitations.

An expression is as follows:

{circumflex over ({dot over (θ)})}_(x)=γ[θ_(x)(λ,{circumflex over (F)}_(x))−C ₁·{circumflex over (θ)}]

{circumflex over ({dot over (θ)})}_(y) =k ₁ sgn({circumflex over (M)}_(k))·(M _(k) −Ĉ ₂ {circumflex over (M)} _(k))+k ₂ sgn(â _(y))·(a _(y)−Ĉ ₂ â _(y))

{circumflex over ({dot over (θ)})}={circumflex over ({dot over(θ)})}_(x)+{circumflex over ({dot over (θ)})}_(y)

A hardware device of the present invention requires sensors, includingGPS, inertial elements and steering wheel rotation angle and torquesensors, and uses mass-produced electric controllers for the wholevehicle for data sampling, so as to implement on-line estimation by thealgorithms designed in Steps 1 and 2. The fuzzy logic designed in Step 3is burned into a controller in the form of a query table to obtain finalfusion estimation results.

PARAMETER DESCRIPTION OF THE EMBODIMENTS

The superscript {circumflex over ( )} represents an estimated value, thesuperscript · represents a first derivative, the subscript x representsa longitudinal direction, and the subscript y represents a lateraldirection.

The above are merely specific embodiments of the present invention,however, the protection scope of the present invention is not limitedthereto. Anyone who familiar with the technical field can easilyconceive various equivalent modifications or substitutions within thetechnical scope revealed by the present invention. These modificationsor substitutions should be included within the protection scope of thepresent invention. Therefore, the protection scope of the presentinvention should be subject to the protection scope of the claims.

1. A method of an adaptive estimation of an adhesion coefficient of avehicle road surface considering complex excitation conditions, themethod comprising the following steps: 1) designing an estimator basedon a single-wheel dynamical model of a whole vehicle, and estimating apeak adhesion coefficient of a road surface under a longitudinal tireforce and a longitudinal excitation; 2) designing an estimator based ona two-degree-of-freedom kinematic model of the whole vehicle, andestimating the peak adhesion coefficient of the road surface under atire aligning torque and a lateral force excitation; 3) determiningexcitation conditions met by a vehicle from vehicle state parameters,obtaining limits that current longitudinal and lateral tire forces canreach by a fuzzy inference, and thereby designing a fusion observer tofuse estimation results.
 2. The method of the adaptive estimation of theadhesion coefficient of the vehicle road surface considering complexexcitation conditions according to claim 1, wherein in the step 1), thesingle-wheel dynamical model of the whole vehicle is as follows:${\overset{.}{\omega} = {\frac{1}{I_{w}}\left\lbrack {T_{m} - {{\mu_{x}\left( {\theta_{x},\lambda} \right)} \cdot F_{z} \cdot R}} \right\rbrack}}{\lambda = \left\{ \begin{matrix}{\frac{{\omega \cdot R} - v_{x}}{\omega \cdot R};{v_{x} < {\omega \cdot R}}} \\{\frac{v_{x} - {\omega \cdot R}}{v_{x}};{v_{x} \geq {\omega \cdot R}}}\end{matrix} \right.}$ wherein ω is an angular velocity of a wheel, {dotover (ω)} is an angular acceleration of the wheel, R is a radius of thewheel, T_(m) is a driving/braking torque acting on the wheel, F_(z) is avertical load acting on the wheel, I_(w) is a rotational inertia of thewheel, λ is a slip rate of the wheel, v_(x) is a longitudinal speed at acenter of the wheel, and μ_(x)(θ_(x),λ) is a current utilizationadhesion coefficient of a tire to the road surface obtained based on atire model.
 3. The method of the adaptive estimation of the adhesioncoefficient of the vehicle road surface considering complex excitationconditions according to claim 2, wherein an expression of the tire modelis as follows:${\mu\left( {\theta,\ \lambda} \right)} = {\theta - {\theta e}^{{- \frac{c_{1}}{\theta}}{({\lambda + {c_{2}\lambda^{2}}})}} - {c_{3}{{\lambda{sgn}}(\lambda)}} + {c_{4}\lambda^{2}}}$wherein θ is the peak adhesion coefficient of the road surface, i.e.,the peak adhesion coefficient of the road surface corresponding to ahighest point of a μ−λ curve, c₁ is a longitudinal slip stiffness of thetire, i.e., a slope of the μ−λ curve at an origin, and c₂, c₃, and c₄are respectively control parameters for a descending section of a curveof the peak adhesion coefficient of the road surface versus the sliprate.
 4. The method of the adaptive estimation of the adhesioncoefficient of the vehicle road surface considering complex excitationconditions according to claim 2, wherein in the step 1), an expressionfor estimating the peak adhesion coefficient of the road surface underthe longitudinal tire force and the longitudinal excitation is asfollows:${{\overset{\hat{}}{F}}_{x} = {{\frac{I_{w}}{R}\left( {y + {K\omega}} \right)} - {F_{z} \cdot {\mu_{x}\left( {{\overset{\hat{}}{\theta}}_{x},\lambda} \right)}}}}{\overset{.}{y} = {{{- \frac{K}{I_{w}}}\left( {T_{m} + {R{\overset{\hat{}}{F}}_{x}}} \right)} + {\frac{R}{I_{w}} \cdot \frac{\partial{\mu_{x}\left( {{\overset{\hat{}}{\theta}}_{x},\lambda} \right)}}{\partial\theta_{x}} \cdot {\overset{\overset{.}{\hat{}}}{\theta}}_{x}}}}{{\overset{\overset{.}{\hat{}}}{\theta}}_{x} = {\gamma\left\lbrack {{\theta_{x}\left( {\lambda,{\overset{\hat{}}{F}}_{x}} \right)} - {\overset{\hat{}}{\theta}}_{x}} \right\rbrack}}$wherein {circumflex over (F)}_(x) is an estimated value of a tirelongitudinal force, μ_(x)({circumflex over (θ)}_(x),λ) is a utilizationadhesion coefficient calculated based on an estimated value of theadhesion coefficient of the road surface and the slip rate, K is a gainof a longitudinal force estimator, θ_(x)(λ,{circumflex over (F)}_(x)) isthe peak adhesion coefficient of the road surface calculated from acurve described by the tire model based on a current longitudinal forceand the slip rate, {circumflex over (θ)}_(x) is an estimated value ofthe peak adhesion coefficient of the road surface under the longitudinalexcitation, γ is a gain of an adhesion coefficient estimator of the roadsurface, y is an intermediate variable, {dot over (y)} is a derivativeof y with respect to time, and {circumflex over ({dot over (θ)})}, is aderivative of θ _(x) with respect to time.
 5. The method of the adaptiveestimation of the adhesion coefficient of the vehicle road surfaceconsidering complex excitation conditions according to claim 4, whereinin the step 2), the two-degree-of-freedom kinematic model of the wholevehicle is as follows:${\alpha_{f} = {\beta + \frac{l_{f}R}{v_{0}} - \delta}}{\alpha_{r} = {\beta - \frac{l_{r}R}{v_{0}}}}$wherein δ is a rotation angle of a front wheel, l_(f) and l_(r) arerespectively a distance from a center of the front wheel and of a rearwheel to a center of mass, v₀ is a longitudinal speed of the vehicle, βis a side slip angle of the vehicle, α_(f) and α_(r) are respectively aslip angle of the front wheel and of the rear wheel, and R is the radiusof the wheel.
 6. The method of the adaptive estimation of the adhesioncoefficient of the vehicle road surface considering complex excitationconditions according to claim 5, wherein in the step 2), an expressionfor estimating the peak adhesion coefficient of the road surface underthe tire aligning torque and the lateral force excitation is as follows:{circumflex over (M)} _(k) =A{dot over (δ)} _(w) +B{umlaut over (δ)}_(w) +i _(s)(δ_(w))M _(s) +i _(m)(δ_(w))M _(m){circumflex over (M)} _(k) =f(α,F _(z)){circumflex over ({dot over (θ)})}_(y) =k ₁ sgn({circumflex over (M)}_(k))·(M _(k) −{circumflex over (M)} _(k))+k ₂ sgn(â _(y))·(a _(y) −â_(y)) wherein α is a slip angle of the wheel, δ_(w) is a rotation angleof a steering wheel, i_(s)(δ_(w)) is a torque-to-rotation ratio of abooster motor to a master pin, i_(m)(δ_(w)) is a torque-to-rotationratio of the steering wheel to the master pin, M_(m) is a torque appliedto the steering wheel, M_(s) is a torque of the booster motor, A and Bare fitting parameters, M_(k) is a fitting total aligning torque,{circumflex over (M)}_(k) is an estimated value of an aligning torquecalculated based on the vertical load of the wheel and the slip angle ofthe wheel, F_(z) is the vertical load applied on the wheel, â_(y) is anestimated value of a lateral acceleration of the vehicle, a_(y) is anactual value of the lateral acceleration of the vehicle, k and k₂ aregains of estimators, {circumflex over (θ)}_(y) is an estimated value ofthe peak adhesion coefficient of the road surface under the lateralforce excitation, and {circumflex over ({dot over (θ)})}_(y) is aderivative of {circumflex over (θ)}_(y) with respect to time.
 7. Themethod of the adaptive estimation of the adhesion coefficient of thevehicle road surface considering complex excitation conditions accordingto claim 6, wherein the step 3) comprises: 31) obtaining a vehicleexcitation state by the fuzzy inference; 32) performing the adaptiveestimation of the peak adhesion coefficient of the road surface under acomplex excitation.
 8. The method of the adaptive estimation of theadhesion coefficient of the vehicle road surface considering complexexcitation conditions according to claim 7, wherein the step 31) is asfollows: inputting a membership function, taking a slip rate referenceλ/C_(λ) and a slip angle reference α/C_(α) as input quantities, whereinC_(λ) and C_(α) are catastrophe points at which tire characteristicsenter a nonlinear zone and are respectively taken as a correspondingslip rate and the slip angle at which the peak adhesion coefficient isreached, and taking Ĉ₁, Ĉ₂ of different estimators as output quantities;setting [0,1] as a domain of both the input quantities and the outputquantities; and dividing the domain into corresponding intervalsrespectively having small, medium and large fuzzy membership degrees. 9.The method of the adaptive estimation of the adhesion coefficient of thevehicle road surface considering complex excitation conditions accordingto claim 8, wherein in the step 32), an expression for performing theadaptive estimation of the peak adhesion coefficient of the road surfaceunder a complex excitation is as follows:{circumflex over ({dot over (θ)})}={circumflex over ({dot over(θ)})}_(x)+{circumflex over ({dot over (θ)})}_(y){circumflex over ({dot over (θ)})}_(x)=γ[θ_(x)(λ,{circumflex over (F)}_(x))−C ₁·{circumflex over (θ)}]{circumflex over ({dot over (θ)})}_(y) =k ₁ sgn({circumflex over (M)}_(k))·(M _(k) −Ĉ ₂ {circumflex over (M)} _(k))+k ₂ sgn(â _(y))·(a _(y)−Ĉ ₂ â _(y)) wherein Ĉ₁ is a representative value of a longitudinalsliding degree of the wheel, Ĉ₂ is a representative value of a side slipdegree of the wheel, and {circumflex over (θ)} is an estimated value ofthe peak adhesion coefficient of the road surface.